Field
Certain aspects of the present disclosure generally relate to machine learning and, more particularly, to improving systems and methods of motion planning.
Background
Mobile agents (e.g., robots) are often faced with unidentified objects and obstacles while moving around an environment. When choosing actions to take in an environment, agents can choose actions that decrease their distance to a goal state, increase their information about the environment, and avoid obstacle collisions, or some combination thereof.
Gathering information about the environment is an important objective when attempting to take actions with limited resources or in unknown environments. One type of environmental information that can be useful in oft traversed domains is the shape and extent of objects in the domain. For the purposes of navigation, the rough extent of an object on the order of the size of the agent itself is useful. Information about the shape and extent of an object at this scale is often gathered through depth-based sensors such as structured light, laser and radar types of sensing technologies or through depth calculations made by co-registering features in many camera views (either from more than one camera or one camera at different points in time). However, most techniques for estimating the shape and extent of objects simply take advantage of movements by the agent, but do not prioritize movements that would be advantageous to the estimation of the shape and extent of an object.